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A Time-Frequency Algorithm for Noisy ICA

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Geo-Informatics in Resource Management and Sustainable Ecosystem (GRMSE 2015)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 569))

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Abstract

The performance of standard algorithms for Independent Component Analysis (ICA) quickly deteriorates when the signals are contaminated by additive noise. In this paper, we propose an ICA approach exploiting the difference in the time-frequency (t-f) signatures of noisy signals to be separated. The approach uses a high-resolution t-f distribution to obtain the t-f matrices of mixed signals, then localizes the signal energy by Hough transform and obtains the estimated signals based on the diagonalization of a combined set of auto-term matrices. Furthermore, its performance is evaluated using the Signal-Noise-Ratio (SNR) as it is commonly employed to assess the ICA algorithms. Both the results of mathematical analysis and numerical simulations indicate that we could enhance the ICA performance by improving the input SNR or increasing the number of sampling points. The approach could increase the ICA robustness by spreading the noise power and localizing the source energy in the t-f domain.

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Acknowledgments

This work was supported by the Fundamental Research Funds for the Central Universities (No. XDJK2014C015), and the Doctoral Funds of Southwest University (No. SWU112056).

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Authors and Affiliations

  1. College of Electronics and Information Engineering, Southwest University, Chongqing, 400715, China

    Jing Guo

  2. Chongqing College of Humanities, Science and Technology Chongqing, Chongqing, China

    Ying Deng

Authors
  1. Jing Guo
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  2. Ying Deng
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Corresponding author

Correspondence to Jing Guo .

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Editors and Affiliations

  1. Wuhan University, Wuhan, China

    Fuling Bian

  2. Eastern Michigan University, Ypsilanti, China

    Yichun Xie

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Guo, J., Deng, Y. (2016). A Time-Frequency Algorithm for Noisy ICA. In: Bian, F., Xie, Y. (eds) Geo-Informatics in Resource Management and Sustainable Ecosystem. GRMSE 2015. Communications in Computer and Information Science, vol 569. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-49155-3_36

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  • DOI: https://doi.org/10.1007/978-3-662-49155-3_36

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-49154-6

  • Online ISBN: 978-3-662-49155-3

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